Opening up a plethora of R functionalities to Excel users with BERT add-in.

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Excel has been the go-to data analytics tool for businesses for the last three decades. Excel provides built-in tools to conduct statistical analysis, creating budgets, forecasting, dashboards, data plotting, etc. And then there are add-ins to fill in the analytical gap or improve performance.

For me, Excel had been a great tool, but I had two issues using it: first being the occasional freezing of Excel when working with large data files (> 50MB), and second, the poor quality of default plots generated which required a lot of polishing. …

Basic concepts covered from data prep to connecting UI and server to publishing the app online

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Shiny is one of the powerful tools in the hand of data analysts and data scientists to develop web-based applications and interactive data visualizations. The Shiny app consists of two important functions: UI and the server function. A key feature of Shiny is the use of reactive programming to automatically update the output when changes are made in the input.

We work on the dataset of the Food and Agriculture Organization (FAO) of the United Nations to get hands-on experience of building a Shiny app from scratch (link for the app). The article is divided into the following sections:

  1. Shiny…

Tee, exposition, and assignment pipe operators for clean code writing

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Apart from hosting the main pipe operator %>% used by the Tidyverse community, the magrittr package in Tidyverse holds a few other pipe operators. The %>% pipe is widely used for data manipulations and is automatically loaded with Tidyverse.

The pipe operator is used to execute multiple operations that are in sequence requiring the output of the previous operation as their input argument. So, the execution starts from the left-hand side with the data as the first argument that is passed to the function on its right and so on. …

Customize ggplot2 themes to create your personalized theme templates for efficient workflow

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This is the last article from the series master the data visualization using the ggplot2 package. The complete list of tutorials are as follows:

  1. Scatter and box plots (link)
  2. Histograms, Bar, and Density plots (link)
  3. Circular plots (pie charts, spider plots, and bar plots) (link)
  4. Theme(): customize to increase workflow

Theme customization is key to increasing work efficiency for those who are regularly changing the default theme settings to make their visualizations more attractive. The default theme used by ggplot2 package is theme_gray(). So, for this tutorial, we will use the theme_gray() function to create our own customized function, theme_customized()

Your go-to guide on creating pie charts, spider plots, and circular bar plots in R

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In the third part of the data visualization series with ggplot2, we will focus on circular plots. The list of the tutorials are as follows:

  1. Scatter and box plots
  2. Histograms, Bar, and Density plots
  3. Circular plots (pie charts, spider plots, and bar plots)
  4. theme(): create your own theme() for increased workflow

So, under circular visualizations, we will be covering on how to create the following charts:

  1. Pie charts
  2. Spider charts
  3. Circular bar plots

Further, we will discuss the pros and cons of using these types of visualizations.

With great power comes great responsibility, use pie and spider charts wisely.

Data and packages


Visualizations made from the first 1000 digits of Pi on the occasion of Pi day.

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Apart from being the birthday of Albert Einstein, March 14 has a special significance. This day also has a nerdy twist to it being written as 3.14, being the approximation of pi constant, the day is officially celebrated as π day. Often people celebrate the day by baking or eating pie.

The mathematical constant is calculated by taking the ratio of the circumference of the circle to its diameter. In 2019, Google’s employee Emma Haruka Iwao, broke the Guinness Book of World Record by calculating the value of pi to the 31 trillion digits. The precise number of digit calculated…

I can understand your situation, this was my first encounter with CSS. At the moment, I don't have much knowledge to the subject matter to even think about writing on CSS. There might be some posts in other Medium publications.

All the best.

Making impressive resume under 1 hour with pagedown package in R

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In the past, I always dreamt of making my resume using LaTeX, but the learning curve to achieve that polished looked never materialized. So, I always ended up making my resume using word documents and silently endured the alignment struggles that entailed.

Recently, I was going through the same cycle of updating my resume and spent a lot of time on alignment. Then I thought, let’s see if there is a permanent solution to this, and by chance, I pondered upon this lovely blog by Nick Strayer, which provides a very simple solution to automate resume building by dividing sections…


Your guide on when and how to use histograms, bar, and density plots in R

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This is the second in the series on creating data visualizations using ggplot2 package. The list of the tutorials are as follows:

  1. Scatter and box plots
  2. Histograms, Bar, and Density plots
  3. Circular plots (pie charts, spider plots, and bar plots)
  4. theme(): create your own theme() for increased workflow

In this article, we will cover the difference between the histograms, bar plots, and density plots and when to use them, and how to plot the different variations.

The major difference between histograms and bar plots is that histograms are used to plot the frequency distribution of quantitative variables while bar plots…

Abhinav Malasi

Passionate about Dataviz | Mathviz | Machine Learning using R. In quest to help SME’s to leverage the power of data.

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